Journal of X-Ray Science and Technology - Volume 31, issue 1

Authors: Dierks, Hanna | Stjärneblad, Philip | Wallentin, Jesper

Article Type: Research Article

Abstract: BACKGROUND: X-ray micro-tomography (μCT) is a powerful non-destructive 3D imaging method applied in many scientific fields. In combination with propagation-based phase-contrast, the method is suitable for samples with low absorption contrast. Phase contrast tomography has become available in the lab with the ongoing development of micro-focused tube sources, but it requires sensitive and high-resolution X-ray detectors. The development of novel scintillation detectors, particularly for microscopy, requires more flexibility than available in commercial tomography systems. OBJECTIVE: We aim to develop a compact, flexible, and versatile μCT laboratory setup that combines absorption and phase contrast imaging as well as the …option to use it for scintillator characterization. Here, we present details on the design and implementation of the setup. METHODS: We used the setup for μCT in absorption and propagation-based phase-contrast mode, as well as to study a perovskite scintillator. RESULTS: We show the 2D and 3D performance in absorption and phase contrast mode, as well as how the setup can be used for testing new scintillator materials in a realistic imaging environment. A spatial resolution of around 1.3μm is measured in 2D and 3D. CONCLUSIONS: The setup meets the needs for common absorption μCT applications and offers increased contrast in phase contrast mode. The availability of a versatile laboratory μCT setup allows not only for easy access to tomographic measurements, but also enables a prompt monitoring and feedback beneficial for advances in scintillator fabrication. Show more

Keywords: X-ray imaging, tomography, μCT, phase contrast, scintillator, laboratory setups

DOI: 10.3233/XST-221294

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 1-12, 2023

Authors: Wei, Qiuyue | Ma, Shenlan | Tang, Shaojie | Li, Baolei | Shen, Jiandong | Xu, Yuanfei | Fan, Jiulun

Article Type: Research Article

Abstract: Several limitations in algorithms and datasets in the field of X-ray security inspection result in the low accuracy of X-ray image inspection. In the literature, there have been rare studies proposed and datasets prepared for the topic of dangerous objects segmentation. In this work, we contribute a purely manual segmentation for labeling the existing X-ray security inspection dataset namely, SIXRay, with the pixel-level semantic information of dangerous objects. We also propose a composition method for X-ray security inspection images to effectively augment the positive samples. This composition method can quickly obtain the positive sample images using affine transformation and HSV …features of X-ray images. Furthermore, to improve the recognition accuracy, especially for adjacent and overlapping dangerous objects, we propose to combine the target detection algorithm (i.e., the softer-non maximum suppression, Softer-NMS) with Mask RCNN, which is named as the Softer-Mask RCNN. Compared with the original model (i.e., Mask RCNN), the Softer-Mask RCNN improves by 3.4% in accuracy (mAP), and 6.2% with adding synthetic data. The study result indicates that our proposed method in this work can effectively improve the recognition performance of dangerous objects depicting in the X-ray security inspection images. Show more

Keywords: Deep learning, data augmentation, mask RCNN, security inspection, object recognition

DOI: 10.3233/XST-221210

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 13-26, 2023

Authors: Sahli, Hanene | Ben Slama, Amine | Zeraii, Abderrazek | Labidi, Salam | Sayadi, Mounir

Article Type: Research Article

Abstract: Computerized segmentation of brain tumor based on magnetic resonance imaging (MRI) data presents an important challenging act in computer vision. In image segmentation, numerous studies have explored the feasibility and advantages of employing deep neural network methods to automatically detect and segment brain tumors depicting on MRI. For training the deeper neural network, the procedure usually requires extensive computational power and it is also very time-consuming due to the complexity and the gradient diffusion difficulty. In order to address and help solve this challenge, we in this study present an automatic approach for Glioblastoma brain tumor segmentation based on deep …Residual Learning Network (ResNet) to get over the gradient problem of deep Convolutional Neural Networks (CNNs). Using the extra layers added to a deep neural network, ResNet algorithm can effectively improve the accuracy and the performance, which is useful in solving complex problems with a much rapid training process. An additional method is then proposed to fully automatically classify different brain tumor categories (necrosis, edema, and enhancing regions). Results confirm that the proposed fusion method (ResNet-SVM) has an increased classification results of accuracy (AC = 89.36%), specificity (SP = 92.52%) and precision (PR = 90.12%) using 260 MRI data for the training and 112 data used for testing and validation of Glioblastoma tumor cases. Compared to the state-of-the art methods, the proposed scheme provides a higher performance by identifying Glioblastoma tumor type. Show more

Keywords: Glioblastoma, MRI images, segmentation, ResNet, SVM classifier

DOI: 10.3233/XST-221240

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 27-48, 2023

Authors: Tan, Xiaoying | Yang, Xiao | Hu, Shudong | Ge, Yuxi | Wu, Qiong | Wang, Jun | Sun, Zongqiong

Article Type: Research Article

Abstract: PURPOSE: To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment. MATERIALS AND METHODS: The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n  = 120) and validation set (n  = 60) and are categorized into effective group (n  = 83) and ineffective group (n  = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of …region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model. RESULTS: Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model. CONCLUSION: CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment. Show more

Keywords: Advanced gastric cancer, neoadjuvant chemotherapy, radiomics, nomogram, Computed tomography

DOI: 10.3233/XST-221291

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 49-61, 2023

Authors: Ren, Junru | Liang, Ningning | Yu, Xiaohuan | Wang, Yizhong | Cai, Ailong | Li, Lei | Yan, Bin

Article Type: Research Article

Abstract: Purpose: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. Methods: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed …sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. Results: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Conclusion: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis. Show more

Keywords: Low-dose computed tomography, prior image compressed sensing, subspace identification, block-matching frames

DOI: 10.3233/XST-221262

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 63-84, 2023

Authors: Yang, Yunfeng | Wu, Huihui

Article Type: Research Article

Abstract: Objective: This study aims to develop and test a new image registration method in which full-scale skip connections in the encoding process is added into the registration network, which can predict the deformation field more accurately by retaining more features and information in the decoding process. Methods: Two improved registration networks are connected in series in the registration framework. Each registration network uses the unsupervised learning registration method to predict a small deformation field, and the last two small deformation fields are superimposed to obtain the final deformation field. The model is evaluated by the oasis datasets (brain …T1-weighted MRI images), one image is selected as the fixed image, while 383 images are used as training images and 30 images are used as test images. Wavelet decomposition and reconstruction are also used to enhance the image. Results: Compared with the affine method, the voxelmorph-1 method and the voxelmorph-2 method, applying the new registration network was proposed by this study improves the registration accuracy by 28.6%, 1.2% and 0.2%, respectively. Conclusion: The experimental results demonstrate that the method proposed in this study can improve the accuracy of image registration effectively. Show more

Keywords: Image registration, wavelet decomposition and reconstruction, LDIRnet, CNN

DOI: 10.3233/XST-221252

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 85-94, 2023

Authors: Gong, Changcheng | Liu, Jianxun

Article Type: Research Article

Abstract: Limited-angle computed tomography (CT) imaging is one of the common imaging problems. The reconstructed images often encounter obvious artifacts and structure degradation. In recent years, the recoverability prior of image structure has been widely explored in limited-angle CT reconstruction, and the image quality has been greatly improved. However, the artifacts and structure degradation still exist. In this study, we establish a new reconstruction model based on weighted relative structure (wRS) determined by image gradients, which serves as weights to guide image reconstruction in order to reduce artifacts and preserve structures. Then, we develop an efficient algorithm using a surrogate function …to solve this model. Moreover, this method is compared with some of other popular reconstruction methods, such as anisotropic total variation method and image gradient L 0 norm minimization method and so on. Experiments on digital phantoms, real carved cheese and walnut projection are reported to demonstrate its superiority. Several quantitative indices including RMSE, PSNR, and SSIM of the reconstruction images from 90°-data of FORBILD head phantom are 0.0120, 43.52, and 0.9961. The experimental results indicate that the image obtained by our method is the closest to reference image. By comparing reconstruction images or their residual images, images reconstructed from real CT data, the experimental results of the residual images and the respective quantitative data analysis also demonstrate that the images reconstructed using our new method suffer from less artifacts and structure degradation. Show more

Keywords: Inverse problem, image reconstruction, computed tomography, relative structure, anisotropic total variation

DOI: 10.3233/XST-221256

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 95-117, 2023

Authors: Lifton, Joseph John | Poon, Keng Yong

Article Type: Research Article

Abstract:  X-ray computed tomography (XCT) enables the dimensional measurement and inspection of highly geometrically complex engineering components that are unmeasurable using optical and tactile instruments. Conventional XCT scans use a circular scan trajectory where X-ray projections are acquired with a uniform angular spacing; this approach treats all projections as being of equal importance, in practice, some projections contain more object information than others. In this work we capitalize on this concept by intelligently selecting projections with a view to improve the quality of surface models extracted from an XCT data-set. Our approach relies on using a priori object information to …select X-ray projections in which the surfaces of the object are aligned with a ray-path, thus ensuring the surface of the object is fully sampled. Results are presented showing that the proposed method is able to reduce CAD comparison errors by 16%, reduce surface form error by 3%, and improve edge contrast by 14% for a machined aluminium component. Show more

Keywords: X-ray computed tomography, dimensional metrology, scan optimisation

DOI: 10.3233/XST-221280

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 119-129, 2023

Authors: Zhang, Ziheng | Yang, Minghan | Li, Huijuan | Chen, Shuai | Wang, Jianye | Xu, Lei

Article Type: Research Article

Abstract: Background: With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public’s attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers. Objective: To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model—Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT …imaging technique can be used to acquire low-dose CT images. Methods: In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection. Results: We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score. Conclusions: Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging. Show more

Keywords: Computed tomography, Low-dose imaging, Limited-angle imaging, Deep learning, Generative adversarial networks

DOI: 10.3233/XST-221260

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 131-152, 2023

Authors: Kang, Yang | Wu, Rui | Wu, Sen | Li, Peizheng | Li, Qingpei | Cao, Kun | Tan, Tingting | Li, Yingrui | Zha, Gangqiang

Article Type: Research Article

Abstract: BACKGROUND: In fan beam X-ray imaging applications, several X-ray images sometimes need to be stitched together into a panoramic image because of the size limitations of the detector. OBJECTIVE: This study aims to propose a novel multi-view X-ray digital imaging stitching algorithm (MVS) based on the CdZnTe photon counting linear array detectors to solve the problem of fan beam X-ray stitching deformation. METHODS: The panoramic image is generated in four steps including (1) multi-view projection data acquisition, (2) overlapping positioning, (3) weighted fusion and (4) projected pixel value calculation. Images of a globe and foot are …scanned by fan beam X-rays and a CdZnTe detector. The proposed method is applied to stitch together the scanned images of the globe. Three other methods are also used for comparison. Finally, this MVS algorithm is also used in the stitching of scanned images of the foot. RESULTS: Compared with the 50% stitching accuracy of other methods, the new MVS algorithm reached a stitching accuracy of 94.4%. Image distortion on the globe and feet is also eliminated and thus image quality is significantly improved. CONCLUSIONS: This study proposes a new multi-view X-ray digital imaging stitching algorithm. Study results demonstrate the superiority of this new algorithm and its feasibility in practical applications. Show more

Keywords: X-ray, photon counting, stitching algorithm, CdZnTe detector

DOI: 10.3233/XST-221261

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 153-166, 2023